SOTAVerified

Face Recognition

Facial Recognition is the task of making a positive identification of a face in a photo or video image against a pre-existing database of faces. It begins with detection - distinguishing human faces from other objects in the image - and then works on identification of those detected faces.

The state of the art tables for this task are contained mainly in the consistent parts of the task : the face verification and face identification tasks.

( Image credit: Face Verification )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1501–1550 of 2329 papers

TitleStatusHype
Human Perception of Audio Deepfakes—0
Hybrid Approach to Face Recognition System using Principle component and Independent component with score based fusion process—0
Hyperbolic Face Anti-Spoofing—0
HyperFace: Generating Synthetic Face Recognition Datasets by Exploring Face Embedding Hypersphere—0
HyperFaceNet: A Hyperspectral Face Recognition Method Based on Deep Fusion—0
Hypergraph Laplacian Eigenmaps and Face Recognition Problems—0
Hyp-OC: Hyperbolic One Class Classification for Face Anti-Spoofing—0
ICD-Face: Intra-class Compactness Distillation for Face Recognition—0
ID2image: Leakage of non-ID information into face descriptors and inversion from descriptors to images—0
ID^3: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition—0
Identifying Physically Realizable Triggers for Backdoored Face Recognition Networks—0
Identity-aware Facial Expression Recognition in Compressed Video—0
Identity Overlap Between Face Recognition Train/Test Data: Causing Optimistic Bias in Accuracy Measurement—0
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation—0
IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images—0
I Feel I Feel You: A Theory of Mind Experiment in Games—0
If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces—0
Illumination,Expression and Occlusion Invariant Pose-Adaptive Face Recognition System for Real-Time Applications—0
Illumination-invariant Face recognition by fusing thermal and visual images via gradient transfer—0
Illumination-invariant face recognition from a single image across extreme pose using a dual dimension AAM ensemble in the thermal infrared spectrum—0
Illumination Normalization via Merging Locally Enhanced Textures for Robust Face Recognition—0
Image-based Face Detection and Recognition: "State of the Art"—0
Image-level Classification in Hyperspectral Images using Feature Descriptors, with Application to Face Recognition—0
Image Pixel Fusion for Human Face Recognition—0
Image processing—0
Image Segmentation Algorithms Overview—0
Image Set based Collaborative Representation for Face Recognition—0
Imbalance Robust Softmax for Deep Embeeding Learning—0
Impact of Face Alignment on Face Image Quality—0
Impact of Face Image Quality Estimation on Presentation Attack Detection—0
Impact of Facial Tattoos and Paintings on Face Recognition Systems—0
Impact of Image Context for Single Deep Learning Face Morphing Attack Detection—0
Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation—0
Implementation of Robust Face Recognition System Using Live Video Feed Based on CNN—0
Implementation of Training Convolutional Neural Networks—0
Improved Eigenfeature Regularization for Face Identification—0
Improved Face Representation via Joint Label Classification and Supervised Contrastive Clustering—0
Improved sparse PCA method for face and image recognition—0
Improved Techniques for GAN based Facial Inpainting—0
Improvement of Color Image Analysis Using a New Hybrid Face Recognition Algorithm based on Discrete Wavelets and Chebyshev Polynomials—0
Improving 2D face recognition via fine-level facial depth generation and RGB-D complementary feature learning—0
Improving Defensive Distillation using Teacher Assistant—0
Improving Face Recognition by Clustering Unlabeled Faces in the Wild—0
Improving Face Recognition from Caption Supervision with Multi-Granular Contextual Feature Aggregation—0
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters—0
Improving Heterogeneous Face Recognition with Conditional Adversarial Networks—0
Improving Identity-Robustness for Face Models—0
Improving Makeup Face Verification by Exploring Part-Based Representations—0
Improving precision and recall of face recognition in SIPP with combination of modified mean search and LSH—0
Improving the JPEG-resistance of Adversarial Attacks on Face Recognition by Interpolation Smoothing—0
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All datasetsLFWCFP-FPCASIA-WebFace+masksCelebA+masksMLFWAgeDB-30Color FERETIJB-BCALFWmebeblurfMFW+ (M-M)MFW+ (U-M)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PIC - ArcFaceFNMR [%] @ 10-3 FMR4.38—Unverified
2GhostFaceNetV2-1 (MS1MV3)Accuracy1—Unverified
3SymFace + AdaFace + ResNet100 +WebFace (MS1MV2)Accuracy1—Unverified
4ProdpolyAccuracy1—Unverified
5DiscFaceAccuracy1—Unverified
6ArcFace + MS1MV2 + R100Accuracy1—Unverified
7DCQAccuracy1—Unverified
8AdaFace + WebFace4M + R100Accuracy1—Unverified
9EdgeFace - S (g=0.5)Accuracy1—Unverified
10EdgeFace - XS (g=0.6)Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
1GhostFaceNetV2-1Accuracy0.99—Unverified
2SymFace + AdaFace + ResNet100 +WebFaceAccuracy0.99—Unverified
3ElasticFace-ArcAccuracy0.99—Unverified
4DiscFaceAccuracy0.99—Unverified
5CircleLoss(ours)Accuracy0.96—Unverified
6EdgeFace - S (g=0.5)Accuracy0.96—Unverified
7DCQAccuracy0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1Fine-tuned ArcFaceAccuracy91.47—Unverified
2Fine-tuned FaceNetAccuracy88.06—Unverified
3ArcFaceAccuracy87.95—Unverified
4Fine-tuned VGG-FaceAccuracy86.85—Unverified
5FaceNetAccuracy84.21—Unverified
6VGG-FaceAccuracy79.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Fine-tuned ArcFaceAccuracy95.43—Unverified
2Fine-tuned FaceNetAccuracy93.58—Unverified
3ArcFaceAccuracy91.78—Unverified
4Fine-tuned VGG-FaceAccuracy91.51—Unverified
5FaceNetAccuracy90.96—Unverified
6VGG-FaceAccuracy84.56—Unverified
#ModelMetricClaimedVerifiedStatus
1MS1MV2, R100, SFaceAccuracy91.57—Unverified
2MS1MV2, R100, ArcfaceAccuracy90.57—Unverified
3MS1MV2, R100, CurricularfaceAccuracy90.43—Unverified
4VGGFace2, R50, ArcFaceAccuracy85.95—Unverified
5CASIA-WebFace, R50, CosFaceAccuracy82.52—Unverified
6Private-Asia, R50, ArcFaceAccuracy77.2—Unverified
#ModelMetricClaimedVerifiedStatus
1ProdpolyAccuracy0.98—Unverified
2ElasticFace-CosAccuracy0.98—Unverified
3Transformer loss+ArcFace ResNet100Accuracy0.98—Unverified
4DCQAccuracy0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1VGG based5-class test accuracy99.2—Unverified
2PIC - ArcFaceFNMR [%] @ 10-3 FMR4.22—Unverified
3PIC - MagFaceFNMR [%] @ 10-3 FMR3.92—Unverified
4PIC - QMagFaceFNMR [%] @ 10-3 FMR3.24—Unverified
#ModelMetricClaimedVerifiedStatus
1ElasticFace-CosTAR @ FAR=0.00010.95—Unverified
2ArcFace+CSFMRank-10.95—Unverified
3ArcFace + MS1MV2 + R100Rank-10.95—Unverified
4AdaFace + MS1MV3 + R100TAR @ FAR=0.00010.94—Unverified
#ModelMetricClaimedVerifiedStatus
1ProdpolyAccuracy0.96—Unverified
2ElasticFace-ArcAccuracy0.96—Unverified
3GhostFaceNetV2-1Accuracy0.96—Unverified
#ModelMetricClaimedVerifiedStatus
1PIC - ArcFaceFNMR [%] @ 10-3 FMR10.1—Unverified
2PIC - QMagFaceFNMR [%] @ 10-3 FMR9.78—Unverified
3PIC - MagFaceFNMR [%] @ 10-3 FMR9.75—Unverified
#ModelMetricClaimedVerifiedStatus
1ArcFace + PPLTAR@FAR=0.000178.8—Unverified
2MaskInv-HGTAR@FAR=0.000178.36—Unverified
3FocusFaceTAR@FAR=0.000177.77—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaFace+PPLTAR@FAR=0.000184.27—Unverified
2MaskInv-HGTAR@FAR=0.000183.82—Unverified
3FocusFaceTAR@FAR=0.000183.4—Unverified
#ModelMetricClaimedVerifiedStatus
1PIC - QMagFaceFNMR [%] @ 10-3 FMR0.96—Unverified
2PIC - MagFaceFNMR [%] @ 10-3 FMR0.96—Unverified
3PIC - ArcFaceFNMR [%] @ 10-3 FMR0.05—Unverified
#ModelMetricClaimedVerifiedStatus
1FaceNet+Adaptive ThresholdAverage Accuracy (10 times)84.3—Unverified
2FaceNet+Fixed Threshold (0.2487)Average Accuracy (10 times)80.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Model with Up Convolution + DoG Filter (Aligned)Rank-185—Unverified
2DPMRank-171—Unverified
#ModelMetricClaimedVerifiedStatus
1FaceNet+Adaptive ThresholdAverage Accuracy (10 times)83.79—Unverified
2FaceNet+Fixed Threshold (0.3968)Average Accuracy (10 times)80.72—Unverified
#ModelMetricClaimedVerifiedStatus
1GhostFaceNetV2-1Accuracy0.95—Unverified
2ElasticFace-ArcAccuracy0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1FaceNet+Adaptive ThresholdAverage Accuracy (10 times)76.46—Unverified
2FaceNet+Fixed Threshold (0.3779)Average Accuracy (10 times)53.97—Unverified
#ModelMetricClaimedVerifiedStatus
1Wang et al. [5]Rank-194.5—Unverified
2Multi-taskRank-184.32—Unverified
#ModelMetricClaimedVerifiedStatus
1Model with Up Convolution + DoG FilterRank-187.2—Unverified
2DPMRank-183.73—Unverified
#ModelMetricClaimedVerifiedStatus
1MCNTAR @ FAR=0.010.54—Unverified
#ModelMetricClaimedVerifiedStatus
1GhostFaceNetV2-1Accuracy99.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Model with Up Convolution + DoG FilterRank-188.33—Unverified
#ModelMetricClaimedVerifiedStatus
1Partial FCMFR-ALL97.85—Unverified
#ModelMetricClaimedVerifiedStatus
1FaceTransformer+OctupletLossAccuracy0.95—Unverified